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Previously, we looked at Barrels, a stat combining exit velocity and launch angle to measure how often a batter makes quality hard contact. As much as batters want to hit a Barrel every time, pitchers want to avoid them at all costs. Yet there is some evidence that pitchers do not have the same influence over Barrels as a batter does.
While Khris Davis and J.D. Martinez tied for the league-lead with 69 Barrels hit last year, Mike Fiers led all pitchers by coughing up 55. Neither performance was an outlier, so it seems to take fewer Barrels to lead pitchers in Barrels given up than it does to lead hitters in Barrels hit. This fits well with DIPS theory, which states that batters can do more to influence batted balls than pitchers can.
It's also not fantasy-relevant, as Mike Fiers just isn't that appealing a fantasy option. The rest of the leaderboard consists of names such as James Shields (52), Mike Minor (50), and Jacob Junis (46), all of whom have low-end appeal in our game if they have any at all. Even if a good pitcher finds their way on this list, it doesn't mean what you might think.
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How to Interpret Batted Ball Statistics
Let's look at a hypothetical pitcher we'll call "Pitcher X." Pitcher X had a great 2015 season (2.60 ERA, 3.16 xFIP) allowing 26 Barrels. He only allowed 11 Barrels in 2016, but his numbers regressed to a 3.88 ERA and 4.02 xFIP in roughly half the IP. Pitcher X got rocked in 2017, allowing 45 Barrels en route to a 4.26 ERA and 3.81 xFIP. If you took that as a red flag and avoided Pitcher X in last season's fantasy drafts, you missed out on Gerrit Cole's 2.88 ERA and 3.04 xFIP in 2018. Clearly, there's nothing predictive here.
The rate stat, Brls/BBE, might seem like a better option. The 2018 Brls/BBE leaderboard is full of boring names (Matt Koch 12.8%, Jarlin Garcia 11%, Mike Minor/Mike Magill 10.8%, etc.), so let's look at 2017 data for an interesting example. Jered Weaver tied for the league-lead in rate of Barrels allowed with 11.8%, and he was obviously terrible. The person he tied with was Craig Kimbrel, one of the best relievers in baseball.
The Barrels hardly hurt Kimbrel's final stat line, as he posted an elite 1.43 ERA (1.50 xFIP) with 35 saves in 2017. Kimbrel had previously been great by Brls/BBE, posting a 5.8% mark in 2016 and 4.7% in 2015, so nothing in his track record should have raised a red flag. Indeed, there was no need for a red flag even in retrospect. Kimbrel was great again last season (2.74 ERA, 3.13 xFIP).
Maybe we need to simplify this and just use average airborne exit velocity? Clayton Richard and Mike Koch posted the highest average airborne exit velocity allowed last season with 95.8 mph. The rookie Koch doesn't have any kind of track record, but Richard had never been this bad before (93 mph in 2017, 92.9 in 2016, 91.9 in 2015). Again, there is nothing predictive about these Statcast metrics.
An earlier version of this article explored the same ideas with the examples of Chris Archer and Justin Verlander. Archer is notorious for underperforming his peripherals, but his Statcast metrics have fluctuated enough that they cannot be cited as the reason why. Verlander looks like a clear regression candidate if you trust Statcast, but he's coming off one of the finest seasons of his Hall of Fame career. Both cases argue against the value of Statcast metrics for pitchers.
Conclusion
Ultimately, Statcast metrics such as Barrels and average airborne exit velocity should probably just be ignored for pitcher analysis. These metrics are great for evaluating batters, but I can't get anything out of them for pitchers even with the benefit of hindsight.
That conclusion may make this seem like a worthless article, but it isn't. Every fantasy analyst uses contact quality to credit or penalize pitchers, either through the Statcast numbers above or an approximation such as the Hard% posted on FanGraphs. This type of analysis may explain a pitcher's performance after the fact, but it seems to have zero predictive value. Therefore, there may be a competitive advantage to be gained by ignoring this type of analysis completely. Score it as a win for DIPS theory.
We'll keep exploring Statcast metrics in our next article, centered on Baseball Savant's xStats.
